Gaussian process regression (GPR) models the spatial correlations of the acoustic field through kernel functions, densifying and denoising the received signals for matched-field processing (MFP) localization. The commonly used Gaussian kernel in GPR accounts only for correlations between nearby points when modeling the acoustic field covariance and neglects differences in correlation structures across different receiver points, leading to insufficient stability and accuracy. In this work, a composite periodic kernel (CPK) is proposed as an improved kernel function, whose form more closely resembles the covariance structure of the acoustic field. The method focuses on reconstructing individual modal components and incorporates seabed boundary conditions to constrain the hyperparameter search space, thereby enhancing the stability and prediction accuracy of GPR. Consequently, MFP localization accuracy is further improved. The method is validated under various signal-to-noise ratios, environmental parameter mismatches, source locations, and waveguides, demonstrating superior performance compared to both the Gaussian kernel and original signals.
Accurate free-field acoustic simulations require non-reflective boundary conditions to suppress spurious reflections at the computational domain boundaries. Although several characteristic-based formulations for direct (forward) simulations have been proposed in recent decades, adjoint formulations of such characteristic-based boundary conditions (CBCs) have received limited attention in the literature and lack a comprehensive analysis.This paper presents the derivation and evaluation of adjoint CBCs complementing existing direct CBCs. Both direct and adjoint CBCs are applied to the nonlinear Euler equations and linear acoustic equations in time-domain simulations. The resulting boundary treatments are compared in terms of accuracy, reflection behavior, and direct-adjoint consistency.The CBCs are implemented using a single-point approach with both static and dynamic wave-vector estimates and are optionally combined with a sponge layer. Over the evaluated frequency range of 350-4000Hz, spurious reflections are effectively attenuated in both direct and adjoint simulations. The adjoint CBCs show reflection behavior consistent with their direct counterparts, demonstrating that the proposed formulation provides a consistent open-boundary treatment for adjoint time-domain acoustics.
This study presents a proof-of-concept framework for identifying buried spherical targets and distinguishing shell from solid structure under subcritical-angle incidence with a near-seabed receiver. In the receiver-position-time domain, the parabolic Radon transform (PRT) concentrates the energy of the curved specular-wave trajectory, unlike the straight bottom-reflected wave, into a localized region of the PRT domain, indicating target presence. In the forward-scattering region, shell targets produce frequency-dependent elastic-wave features distinct from solid targets, enabling shell-solid feature extraction. A controlled water-tank experiment using a stainless-steel shell and a SUJ2 solid sphere, both 10cm in diameter, the shell 6mm thick, demonstrates feasibility.
An analytical approach is explored here to study the far-field aerodynamic noise generated by the interaction of an unsteady gust with the blades of a rotor. The gust is generated by the pitching motion of an airfoil present upstream of the rotor, which sheds a vortex sheet that advects downstream towards the rotor. The advection and time-varying circulation of this sheet induce a fluctuating loading force on the rotor blades, which gives rise to far-field aerodynamic noise. An inversion approach is devised to deduce the motion of the gust generator using measurements of far-field pressure fluctuations. For the forward problem, the time-varying circulation for a step change in angle of attack of the pitching airfoil is determined using the Wagner function. A blade-vortex interaction model is used to determine the loading on each rotor blade due to the step gust, and the loading for arbitrary motion of the upstream airfoil is obtained using a convolution operation. The convolution integral is discretized to obtain the inversion expression, whereby measured far-field pressure signals can be inverted to deduce the pitching motion of the upstream airfoil. This provides a non-intrusive framework for characterizing gust-blade interaction dynamics, with potential applications in aeroacoustic diagnostics and rotor noise mitigation strategies. The inversion approach is tested with various levels of Gaussian noise introduced into the far-field pressure to assess the applicability of the proposed approach under non-ideal conditions. The results indicate that the approach is capable of successfully reconstructing a variety of pitching motion profiles such as ramps, sinusoids, and more complex variations, even when moderate levels of background noise are present.
